Credit Default Prediction Based on Multivariate Regression Article Swipe
Yingzi Sun
,
Lirui Yang
,
Ruonan Zhao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2991/978-94-6463-142-5_3
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2991/978-94-6463-142-5_3
Credit default is a wide-spread credit derivative instrument.As it becomes more and more popular, an appropriate supervision system has to be established.In this paper, a multiple factor regression models are constructed in order to investigate the feasibility for credit default prediction based on R program.Since risks are unavoidable, some measures should be taken to predict them in order to help the banks that sell credit default swaps to minimize their risks.According to the analysis, a model is successfully created.These results shed light on guiding further exploration focusing on credit default prediction.
Related Topics
Concepts
Credit default swap
Credit derivative
Multivariate statistics
Credit risk
iTraxx
Credit default swap index
Order (exchange)
Econometrics
Derivative (finance)
Regression
Logistic regression
Computer science
Actuarial science
Credit valuation adjustment
Business
Machine learning
Finance
Economics
Credit reference
Statistics
Mathematics
Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.2991/978-94-6463-142-5_3
- https://www.atlantis-press.com/article/125986734.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4376525920
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4376525920Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2991/978-94-6463-142-5_3Digital Object Identifier
- Title
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Credit Default Prediction Based on Multivariate RegressionWork title
- Type
-
book-chapterOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
-
Yingzi Sun, Lirui Yang, Ruonan ZhaoList of authors in order
- Landing page
-
https://doi.org/10.2991/978-94-6463-142-5_3Publisher landing page
- PDF URL
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https://www.atlantis-press.com/article/125986734.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://www.atlantis-press.com/article/125986734.pdfDirect OA link when available
- Concepts
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Credit default swap, Credit derivative, Multivariate statistics, Credit risk, iTraxx, Credit default swap index, Order (exchange), Econometrics, Derivative (finance), Regression, Logistic regression, Computer science, Actuarial science, Credit valuation adjustment, Business, Machine learning, Finance, Economics, Credit reference, Statistics, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- References (count)
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6Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 19, 33, 53, 58, 67, 71 |
| abstract_inverted_index.and | 11 |
| abstract_inverted_index.are | 29, 46 |
| abstract_inverted_index.for | 37 |
| abstract_inverted_index.has | 18 |
| abstract_inverted_index.the | 35, 60, 72 |
| abstract_inverted_index.help | 59 |
| abstract_inverted_index.more | 10, 12 |
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| abstract_inverted_index.them | 55 |
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| abstract_inverted_index.light | 81 |
| abstract_inverted_index.model | 75 |
| abstract_inverted_index.order | 32, 57 |
| abstract_inverted_index.risks | 45 |
| abstract_inverted_index.swaps | 66 |
| abstract_inverted_index.taken | 52 |
| abstract_inverted_index.their | 69 |
| abstract_inverted_index.Credit | 0 |
| abstract_inverted_index.credit | 5, 38, 64, 88 |
| abstract_inverted_index.factor | 26 |
| abstract_inverted_index.models | 28 |
| abstract_inverted_index.paper, | 23 |
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| abstract_inverted_index.predict | 54 |
| abstract_inverted_index.results | 79 |
| abstract_inverted_index.focusing | 86 |
| abstract_inverted_index.measures | 49 |
| abstract_inverted_index.minimize | 68 |
| abstract_inverted_index.multiple | 25 |
| abstract_inverted_index.popular, | 13 |
| abstract_inverted_index.analysis, | 73 |
| abstract_inverted_index.derivative | 6 |
| abstract_inverted_index.prediction | 40 |
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| abstract_inverted_index.exploration | 85 |
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| abstract_inverted_index.investigate | 34 |
| abstract_inverted_index.prediction. | 90 |
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| abstract_inverted_index.unavoidable, | 47 |
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| abstract_inverted_index.risks.According | 70 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.87537369 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |